Various imaging modalities have been developed to measure biological systems. Two-photon microscopy, for example, is now recognized as a valuable tool for real-time in vivo imaging of biological systems. A two-photon microscope excites fluorophores in a volume of biological sample using pulsed lasers to induce the emission of a fluorescence signal. Typically, a focused laser beam scans the tissue in a two-dimensional raster pattern, producing a fluorescence image that typically spans hundreds of cells. The images facilitate highly informative and quantitative analyses with a range of biological applications.
Properly separating signal from noise, often termed denoising, is a crucial signal processing procedure in the analysis of imaging data. While there has been considerable success in the development of imaging systems, such as two-photon microscopy, the corresponding signal processing methodology has received less attention. The observed fluorescence response depends upon several factors: 1) the nature of the stimulus and the modulation of neural activity due to the stimulus; 2) movements due to highly structured physiological processes; 3) spontaneous neural activity; and 4) optical and electrical noise. Existing methods for processing two-photon data consist of averaging the measured fluorescence levels over multiple trials followed by kernel-based smoothing or fitting an appropriate curve to these time-series data. Averaging, while highly intuitive and easy to perform, requires a large number of trials which is often not possible in two-photon imaging measurements.
Thus, there is a continuing need for improvements in the processing of image data to improve the speed and usefulness of such systems and methods.